Eugene Shevchuk
Working on "Machine Learning with Python" MIT course projects, I successfully applied key ML methodologies and happy to share the code. Due to MIT policy, I can not store it accessible publicly, but I am eligible to share with you as a potential employer directly upon your request
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I know
- Principle component analysis
- Convolutional neural networks
- Gaussian Mixture model (EM kernalization)
- Collaborative filtering
- Reinforcement learning (MDP + NN)
My projects
MIT projects I enjoyed most
Digits recognition
MNIST data set. Single and double overlapping digits
Movies scores prediction
Netflix data set. 1-5 ✩ score per user prediction
AI playing computer game
Model learn text game rules & strategy by playing it
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Projects 1-2
Digits recognition
Goal: Recognize handwritted digits
Result: minimal error rate 2% for two overlapping digits recognition
Input data:
MNIST Dataset - handwritten single & double overlapping digits
- 60K - training, 10K - testing
→ View the code
Result: minimal error rate 2% for two overlapping digits recognition
Input data:
MNIST Dataset - handwritten single & double overlapping digits
- 60K - training, 10K - testing
→ View the code
Single digit recognition
(manually coded)
(manually coded)
Multinomial (softmax) regression with PCA & cubic features
PCA dimensions reduction 784 ⤑ 18
Kernel: $\phi (x)^ T \phi (x') = (x^ T x' + 1)^3$
Test error: 0.08520
Kernel: $\phi (x)^ T \phi (x') = (x^ T x' + 1)^3$
Test error: 0.08520
View the code →
Convolutional Neural Network
Optimized over:
- baseline (no modifications)
- batch size
- learning rate
- momentum
- activation (ReLU / LeakyReLU)
Test error: 0.9902
- baseline (no modifications)
- batch size
- learning rate
- momentum
- activation (ReLU / LeakyReLU)
Test error: 0.9902
Double digit recognition
(PyTorch)
(PyTorch)
Project 3
Netflix ratings prediction
Goal: Predict unrated movies
Result: $\Delta \geqslant 2$ error rate 0.17
Input data:
1200 users for 1200 movies
Ratings values $\in {1, ..., 5}$
Value $= 0$ for unrated ones
Description:
Result: $\Delta \geqslant 2$ error rate 0.17
Input data:
1200 users for 1200 movies
Ratings values $\in {1, ..., 5}$
Value $= 0$ for unrated ones
Description:
I was given a data matrix containing movie ratings made by users extracted from Netflix database. Any particular user has rated only a small fraction of the movies so the data matrix was only partially filled. The goal was to predict all the remaining entries of the matrix. I approached it by building a Gaussian Mixture Model (GMM) for collaborative filtering educating it with Expectation Maximization algorithm.
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